{
  "id": "1811.07006",
  "version": "v2",
  "published": "2018-11-16T19:51:43.000Z",
  "updated": "2018-12-03T04:19:46.000Z",
  "title": "Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights",
  "authors": [
    "Melanie F. Pradier",
    "Weiwei Pan",
    "Jiayu Yao",
    "Soumya Ghosh",
    "Finale Doshi-velez"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "While modern neural networks are making remarkable gains in terms of predictive accuracy, characterizing uncertainty over the parameters of these models (in a Bayesian setting) is challenging because of the high-dimensionality of the network parameter space and the correlations between these parameters. In this paper, we introduce a novel framework for variational inference for Bayesian neural networks that (1) encodes complex distributions in high-dimensional parameter space with representations in a low-dimensional latent space and (2) performs inference efficiently on the low-dimensional representations. Across a large array of synthetic and real-world datasets, we show that our method improves uncertainty characterization and model generalization when compared with methods that work directly in the parameter space.",
  "revisions": [
    {
      "version": "v2",
      "updated": "2018-12-03T04:19:46.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "neural network weights",
      "avoiding weight-space pathologies",
      "learning latent representations",
      "projected bnns",
      "encodes complex distributions"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}